2012.11.01- slide 1is 257 – fall 2012 data warehouses, decision support and data mining university...

2012.11.01- SLIDE 1IS 257 – Fall 2012

Data Warehouses, Decision Support and Data Mining

University of California, Berkeley

School of Information

IS 257: Database Management

2012.11.01- SLIDE 2IS 257 – Fall 2012

Lecture Outline

• Review– Data Warehouses

• (Based on lecture notes from Joachim Hammer, University of Florida, and Joe Hellerstein and Mike Stonebraker of UCB)

• Views and View Maintenance• Applications for Data Warehouses

– Decision Support Systems (DSS)– OLAP (ROLAP, MOLAP)– Data Mining

• Thanks again to lecture notes from Joachim Hammer of the University of Florida

• A new architecture – SAP HANA

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Lecture Outline






2012.11.01- SLIDE 4IS 257 – Fall 2012

Problem: Heterogeneous Information Sources

“Heterogeneities are everywhere”

Different interfaces Different data representations Duplicate and inconsistent information

PersonalDatabases

Digital Libraries

Scientific DatabasesWorldWideWeb

Slide credit: J. Hammer

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Problem: Data Management in Large Enterprises

• Vertical fragmentation of informational systems (vertical stove pipes)

• Result of application (user)-driven development of operational systems

Sales Administration Finance Manufacturing ...

Sales PlanningStock Mngmt

...

Suppliers

...Debt Mngmt

Num. Control

...Inventory


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Goal: Unified Access to Data

Integration System

• Collects and combines information• Provides integrated view, uniform user interface• Supports sharing

WorldWideWeb

Digital Libraries Scientific Databases

PersonalDatabases


2012.11.01- SLIDE 7IS 257 – Fall 2012

The Traditional Research Approach

Source SourceSource. . .

Integration System

. . .

Metadata

Clients

Wrapper WrapperWrapper

• Query-driven (lazy, on-demand)


2012.11.01- SLIDE 8IS 257 – Fall 2012

The Warehousing Approach

DataWarehouse

Clients

Source SourceSource. . .

Extractor/Monitor

Integration System

. . .

Metadata

Extractor/Monitor

Extractor/Monitor

• Information integrated in advance

• Stored in WH for direct querying and analysis


2012.11.01- SLIDE 9IS 257 – Fall 2012

What is a Data Warehouse?

“A Data Warehouse is a –subject-oriented,– integrated,– time-variant,–non-volatile

collection of data used in support of management decision making processes.”

-- Inmon & Hackathorn, 1994: viz. Hoffer, Chap 11

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A Data Warehouse is...

• Stored collection of diverse data– A solution to data integration problem– Single repository of information

• Subject-oriented– Organized by subject, not by application– Used for analysis, data mining, etc.

• Optimized differently from transaction-oriented db

• User interface aimed at executive decision makers and analysts

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… Cont’d

• Large volume of data (Gb, Tb)• Non-volatile

– Historical– Time attributes are important

• Updates infrequent• May be append-only• Examples

– All transactions ever at WalMart– Complete client histories at insurance firm– Stockbroker financial information and portfolios


2012.11.01- SLIDE 12IS 257 – Fall 2012

Data Warehousing Architecture

2012.11.01- SLIDE 13IS 257 – Fall 2012

“Ingest”

DataWarehouse

Clients

Source/ File Source / ExternalSource / DB. . .

Extractor/Monitor

Integration System

. . .

Metadata

Extractor/Monitor

Extractor/Monitor

2012.11.01- SLIDE 14IS 257 – Fall 2012

Lecture Outline






2012.11.01- SLIDE 15IS 257 – Fall 2012

Warehouse Maintenance

• Warehouse data materialized view– Initial loading– View maintenance

• View maintenance


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Differs from Conventional View Maintenance...

• Warehouses may be highly aggregated and summarized

• Warehouse views may be over history of base data

• Process large batch updates• Schema may evolve


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Differs from Conventional View Maintenance...

• Base data doesn’t participate in view maintenance– Simply reports changes– Loosely coupled– Absence of locking, global transactions– May not be queriable


2012.11.01- SLIDE 18IS 257 – Fall 2012

Sales Comp.

Integrator

DataWarehouse

Sale(item,clerk) Emp(clerk,age)

Sold (item,clerk,age)

Sold = Sale Emp


Warehouse Maintenance Anomalies

• Materialized view maintenance in loosely coupled, non-transactional environment

• Simple example

2012.11.01- SLIDE 19IS 257 – Fall 2012

1. Insert into Emp(Mary,25), notify integrator

2. Insert into Sale (Computer,Mary), notify integrator

3. (1) integrator adds Sale (Mary,25)4. (2) integrator adds (Computer,Mary) Emp5. View incorrect (duplicate tuple)

Sales Comp.

Integrator

DataWarehouse

Sale(item,clerk) Emp(clerk,age)

Sold (item,clerk,age)


Warehouse Maintenance Anomalies

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Maintenance Anomaly - Solutions

• Incremental update algorithms (ECA, Strobe, etc.)– ECA (Eager Compensating Algorithm) is “an

incremental view maintenance algorithm. It is a method for fixing the view maintenance problem that occurs due to the decoupling between base data and the view maintenance manager at the warehouse”

• Research issues: Self-maintainable views– What views are self-maintainable– Store auxiliary views so original + auxiliary

views are self-maintainable

2012.11.01- SLIDE 21IS 257 – Fall 2012

Sold(item,clerk,age) =Sale(item,clerk) Emp(clerk,age)• Inserts into Emp

– If Emp.clerk is key and Sale.clerk is foreign key (with ref. int.) then no effect

• Inserts into Sale– Maintain auxiliary view: – Emp-clerk,age(Sold)

• Deletes from Emp– Delete from Sold based on clerk

Self-Maintainability: Examples


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Self-Maintainability: Examples

• Deletes from SaleDelete from Sold based on {item,clerk}Unless age at time of sale is relevant

• Auxiliary views for self-maintainability– Must themselves be self-maintainable– One solution: all source data– But want minimal set


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Partial Self-Maintainability

• Avoid (but don’t prohibit) going to sourcesSold=Sale(item,clerk) Emp(clerk,age)

• Inserts into Sale– Check if clerk already in Sold, go to source

if not– Or replicate all clerks over age 30– Or ...


2012.11.01- SLIDE 24IS 257 – Fall 2012

Warehouse Specification (ideally)

Extractor/Monitor

Extractor/Monitor

Extractor/Monitor

Integrator

Warehouse

...

Metadata

Warehouse Configuration

Module

View Definitions

Integrationrules

ChangeDetection

Requirements


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Optimization

• Update filtering at extractor– Similar to irrelevant updates in constraint and

view maintenance• Multiple view maintenance

– If warehouse contains several views– Exploit shared sub-views


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Additional Research Issues

• Historical views of non-historical data• Expiring outdated information• Crash recovery• Addition and removal of information

sources– Schema evolution


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More Information on DW

• Agosta, Lou, The Essential Guide to Data Warehousing. Prentise Hall PTR, 1999.

• Devlin, Barry, Data Warehouse, from Architecture to Implementation. Addison-Wesley, 1997.

• Inmon, W.H., Building the Data Warehouse. John Wiley, 1992.

• Widom, J., “Research Problems in Data Warehousing.” Proc. of the 4th Intl. CIKM Conf., 1995.

• Chaudhuri, S., Dayal, U., “An Overview of Data Warehousing and OLAP Technology.” ACM SIGMOD Record, March 1997.

2012.11.01- SLIDE 28IS 257 – Fall 2012

Lecture Outline






2012.11.01- SLIDE 29IS 257 – Fall 2012

Today

• Applications for Data Warehouses– Decision Support Systems (DSS)– OLAP (ROLAP, MOLAP)– Data Mining

• Thanks again to slides and lecture notes from Joachim Hammer of the University of Florida, and also to Laura Squier of SPSS, Gregory Piatetsky-Shapiro of KDNuggets and to the CRISP web site

Source: Gregory Piatetsky-Shapiro

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Trends leading to Data Flood

• More data is generated:– Bank, telecom, other

business transactions ...– Scientific Data: astronomy,

biology, etc– Web, text, and e-

commerce

• More data is captured:– Storage technology faster

and cheaper– DBMS capable of handling

bigger DB


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Examples

• Europe's Very Long Baseline Interferometry (VLBI) has 16 telescopes, each of which produces 1 Gigabit/second of astronomical data over a 25-day observation session – storage and analysis a big problem

• Walmart reported to have 500 Terabyte DB • AT&T handles billions of calls per day

– data cannot be stored -- analysis is done on the fly


2012.11.01- SLIDE 32IS 257 – Fall 2012

Growth Trends

• Moore’s law– Computer Speed doubles

every 18 months• Storage law

– total storage doubles every 9 months

• Consequence– very little data will ever be

looked at by a human• Knowledge Discovery is

NEEDED to make sense and use of data.


2012.11.01- SLIDE 33IS 257 – Fall 2012

Knowledge Discovery in Data (KDD)

• Knowledge Discovery in Data is the non-trivial process of identifying – valid– novel– potentially useful– and ultimately understandable patterns in

data.• from Advances in Knowledge Discovery and Data

Mining, Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy, (Chapter 1), AAAI/MIT Press 1996


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Related Fields

Statistics

MachineLearning

Databases

Visualization

Data Mining and Knowledge Discovery


2012.11.01- SLIDE 35IS 257 – Fall 2012

______

______

______

Transformed Data

Patternsand

Rules

Target Data

RawData

KnowledgeData MiningTransformation

Interpretation& Evaluation

Selection& Cleaning

Integration

Understanding

Knowledge Discovery Process

DATAWarehouse

Knowledge


2012.11.01- SLIDE 36IS 257 – Fall 2012

What is Decision Support?

• Technology that will help managers and planners make decisions regarding the organization and its operations based on data in the Data Warehouse.– What was the last two years of sales volume

for each product by state and city?– What effects will a 5% price discount have on

our future income for product X?• Increasing common term is KDD

– Knowledge Discovery in Databases

2012.11.01- SLIDE 37IS 257 – Fall 2012

Conventional Query Tools

• Ad-hoc queries and reports using conventional database tools– E.g. Access queries.

• Typical database designs include fixed sets of reports and queries to support them– The end-user is often not given the ability to

do ad-hoc queries

2012.11.01- SLIDE 38IS 257 – Fall 2012

OLAP

• Online Line Analytical Processing– Intended to provide multidimensional views of

the data– I.e., the “Data Cube”– The PivotTables in MS Excel are examples of

OLAP tools

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Data Cube

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Operations on Data Cubes

• Slicing the cube– Extracts a 2d table from the multidimensional

data cube– Example…

• Drill-Down– Analyzing a given set of data at a finer level of

detail

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Star Schema

• Typical design for the derived layer of a Data Warehouse or Mart for Decision Support– Particularly suited to ad-hoc queries– Dimensional data separate from fact or event

data• Fact tables contain factual or quantitative

data about the business• Dimension tables hold data about the

subjects of the business• Typically there is one Fact table with

multiple dimension tables

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Star Schema for multidimensional data

OrderOrderNoOrderDate…

SalespersonSalespersonIDSalespersonNameCityQuota

Fact TableOrderNoSalespersonidCustomernoProdNoDatekeyCitynameQuantityTotalPrice City

CityNameStateCountry…

DateDateKeyDayMonthYear…

ProductProdNoProdNameCategoryDescription…

CustomerCustomerNameCustomerAddressCity…

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Data Mining

• Data mining is knowledge discovery rather than question answering– May have no pre-formulated questions– Derived from

• Traditional Statistics• Artificial intelligence• Computer graphics (visualization)

• Another term used is “Analytics” which covers much of the same topics

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Goals of Data Mining

• Explanatory – Explain some observed event or situation

• Why have the sales of SUVs increased in California but not in Oregon?

• Confirmatory– To confirm a hypothesis

• Whether 2-income families are more likely to buy family medical coverage

• Exploratory– To analyze data for new or unexpected relationships

• What spending patterns seem to indicate credit card fraud?

2012.11.01- SLIDE 45IS 257 – Fall 2012

Data Mining Applications

• Profiling Populations• Analysis of business trends• Target marketing• Usage Analysis• Campaign effectiveness• Product affinity• Customer Retention and Churn• Profitability Analysis• Customer Value Analysis• Up-Selling

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Data + Text Mining Process

Source: Languisticsvia Google Images

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How Can We Do Data Mining?

• By Utilizing the CRISP-DM Methodology– a standard process – existing data– software technologies – situational expertise

Source: Laura Squier

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Why Should There be a Standard Process?

• Framework for recording experience– Allows projects to be

replicated

• Aid to project planning and management

• “Comfort factor” for new adopters– Demonstrates maturity of

Data Mining– Reduces dependency on

“stars”

The data mining process must be reliable and repeatable by people with little data mining background.


2012.11.01- SLIDE 49IS 257 – Fall 2012

Process Standardization

• CRISP-DM: • CRoss Industry Standard Process for Data Mining• Initiative launched Sept.1996• SPSS/ISL, NCR, Daimler-Benz, OHRA• Funding from European commission• Over 200 members of the CRISP-DM SIG worldwide

– DM Vendors - SPSS, NCR, IBM, SAS, SGI, Data Distilleries, Syllogic, Magnify, ..

– System Suppliers / consultants - Cap Gemini, ICL Retail, Deloitte & Touche, …

– End Users - BT, ABB, Lloyds Bank, AirTouch, Experian, ...


2012.11.01- SLIDE 50IS 257 – Fall 2012

CRISP-DM

• Non-proprietary• Application/Industry neutral• Tool neutral• Focus on business issues

– As well as technical analysis

• Framework for guidance• Experience base

– Templates for Analysis


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The CRISP-DM Process Model


2012.11.01- SLIDE 52IS 257 – Fall 2012

Why CRISP-DM?

• The data mining process must be reliable and repeatable by people with little data mining skills

• CRISP-DM provides a uniform framework for – guidelines – experience documentation

• CRISP-DM is flexible to account for differences – Different business/agency problems– Different data


2012.11.01- SLIDE 53IS 257 – Fall 2012

BusinessUnderstanding

DataUnderstanding

EvaluationDataPreparation

Modeling

Determine Business ObjectivesBackgroundBusiness ObjectivesBusiness Success Criteria

Situation AssessmentInventory of ResourcesRequirements, Assumptions, and ConstraintsRisks and ContingenciesTerminologyCosts and Benefits

Determine Data Mining GoalData Mining GoalsData Mining Success Criteria

Produce Project PlanProject PlanInitial Asessment of Tools and Techniques

Collect Initial DataInitial Data Collection Report

Describe DataData Description Report

Explore DataData Exploration Report

Verify Data Quality Data Quality Report

Data SetData Set Description

Select Data Rationale for Inclusion / Exclusion

Clean Data Data Cleaning Report

Construct DataDerived AttributesGenerated Records

Integrate DataMerged Data

Format DataReformatted Data

Select Modeling TechniqueModeling TechniqueModeling Assumptions

Generate Test DesignTest Design

Build ModelParameter SettingsModelsModel Description

Assess ModelModel AssessmentRevised Parameter Settings

Evaluate ResultsAssessment of Data Mining Results w.r.t. Business Success CriteriaApproved Models

Review ProcessReview of Process

Determine Next StepsList of Possible ActionsDecision

Plan DeploymentDeployment Plan

Plan Monitoring and MaintenanceMonitoring and Maintenance Plan

Produce Final ReportFinal ReportFinal Presentation

Review ProjectExperience Documentation

Deployment

Phases and Tasks


2012.11.01- SLIDE 54IS 257 – Fall 2012

Phases in CRISP• Business Understanding

– This initial phase focuses on understanding the project objectives and requirements from a business perspective, and then converting this knowledge into a data mining problem definition, and a preliminary plan designed to achieve the objectives.

• Data Understanding– The data understanding phase starts with an initial data collection and proceeds with activities in order to get familiar with the data,

to identify data quality problems, to discover first insights into the data, or to detect interesting subsets to form hypotheses for hidden information.

• Data Preparation– The data preparation phase covers all activities to construct the final dataset (data that will be fed into the modeling tool(s)) from

the initial raw data. Data preparation tasks are likely to be performed multiple times, and not in any prescribed order. Tasks include table, record, and attribute selection as well as transformation and cleaning of data for modeling tools.

• Modeling– In this phase, various modeling techniques are selected and applied, and their parameters are calibrated to optimal values.

Typically, there are several techniques for the same data mining problem type. Some techniques have specific requirements on the form of data. Therefore, stepping back to the data preparation phase is often needed.

• Evaluation– At this stage in the project you have built a model (or models) that appears to have high quality, from a data analysis perspective.

Before proceeding to final deployment of the model, it is important to more thoroughly evaluate the model, and review the steps executed to construct the model, to be certain it properly achieves the business objectives. A key objective is to determine if there is some important business issue that has not been sufficiently considered. At the end of this phase, a decision on the use of the data mining results should be reached.

• Deployment– Creation of the model is generally not the end of the project. Even if the purpose of the model is to increase knowledge of the data,

the knowledge gained will need to be organized and presented in a way that the customer can use it. Depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data mining process. In many cases it will be the customer, not the data analyst, who will carry out the deployment steps. However, even if the analyst will not carry out the deployment effort it is important for the customer to understand up front what actions will need to be carried out in order to actually make use of the created models.

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Phases in the DM Process: CRISP-DM


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Phases in the DM Process (1 & 2)

• Business Understanding:– Statement of Business Objective– Statement of Data Mining objective– Statement of Success Criteria

• Data Understanding– Explore the data and verify the quality– Find outliers


2012.11.01- SLIDE 57IS 257 – Fall 2012

Phases in the DM Process (3)

• Data preparation:– Takes usually over 90% of our time

• Collection• Assessment• Consolidation and Cleaning

– table links, aggregation level, missing values, etc

• Data selection– active role in ignoring non-contributory data?– outliers?– Use of samples– visualization tools

• Transformations - create new variablesSource: Laura Squier

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• Model building– Selection of the modeling techniques is based

upon the data mining objective– Modeling is an iterative process - different for

supervised and unsupervised learning• May model for either description or prediction


2012.11.01- SLIDE 59IS 257 – Fall 2012

Types of Models

• Prediction Models for Predicting and Classifying– Regression algorithms (predict numeric outcome): neural

networks, rule induction, CART (OLS regression, GLM)– Classification algorithm predict symbolic outcome): CHAID (CHi-

squared Automatic Interaction Detection), C5.0 (discriminant analysis, logistic regression)

• Descriptive Models for Grouping and Finding Associations– Clustering/Grouping algorithms: K-means, Kohonen– Association algorithms: apriori, GRI


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Data Mining Algorithms

• Market Basket Analysis• Memory-based reasoning• Cluster detection• Link analysis• Decision trees and rule induction

algorithms• Neural Networks• Genetic algorithms

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Market Basket Analysis

• A type of clustering used to predict purchase patterns.

• Identify the products likely to be purchased in conjunction with other products– E.g., the famous (and apocryphal) story that

men who buy diapers on Friday nights also buy beer.

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Memory-based reasoning

• Use known instances of a model to make predictions about unknown instances.

• Could be used for sales forecasting or fraud detection by working from known cases to predict new cases

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Cluster detection

• Finds data records that are similar to each other.

• K-nearest neighbors (where K represents the mathematical distance to the nearest similar record) is an example of one clustering algorithm

2012.11.01- SLIDE 64IS 257 – Fall 2012

Kohonen Network

• Description• unsupervised• seeks to

describe dataset in terms of natural clusters of cases


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Link analysis

• Follows relationships between records to discover patterns

• Link analysis can provide the basis for various affinity marketing programs

• Similar to Markov transition analysis methods where probabilities are calculated for each observed transition.

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Decision trees and rule induction algorithms

• Pulls rules out of a mass of data using classification and regression trees (CART) or Chi-Square automatic interaction detectors (CHAID)

• These algorithms produce explicit rules, which make understanding the results simpler

2012.11.01- SLIDE 67IS 257 – Fall 2012

Rule Induction

• Description– Produces decision trees:

• income < $40K– job > 5 yrs then good risk– job < 5 yrs then bad risk

• income > $40K– high debt then bad risk– low debt then good risk

– Or Rule Sets:• Rule #1 for good risk:

– if income > $40K– if low debt

• Rule #2 for good risk:– if income < $40K– if job > 5 years


2012.11.01- SLIDE 68IS 257 – Fall 2012

Rule Induction

• Description• Intuitive output• Handles all forms of numeric data, as well

as non-numeric (symbolic) data

• C5 Algorithm a special case of rule induction

• Target variable must be symbolic


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Apriori

• Description• Seeks association rules in dataset• ‘Market basket’ analysis• Sequence discovery


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Neural Networks

• Attempt to model neurons in the brain• Learn from a training set and then can be

used to detect patterns inherent in that training set

• Neural nets are effective when the data is shapeless and lacking any apparent patterns

• May be hard to understand results

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Neural Network

Output

Hidden layer

Input layer


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Neural Networks

• Description– Difficult interpretation– Tends to ‘overfit’ the data– Extensive amount of training time– A lot of data preparation– Works with all data types


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Genetic algorithms

• Imitate natural selection processes to evolve models using– Selection– Crossover– Mutation

• Each new generation inherits traits from the previous ones until only the most predictive survive.

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• Model Evaluation– Evaluation of model: how well it

performed on test data– Methods and criteria depend on

model type:• e.g., coincidence matrix with

classification models, mean error rate with regression models

– Interpretation of model: important or not, easy or hard depends on algorithm


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• Deployment– Determine how the results need to be utilized– Who needs to use them?– How often do they need to be used

• Deploy Data Mining results by:– Scoring a database– Utilizing results as business rules– interactive scoring on-line


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Specific Data Mining Applications:


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What data mining has done for...

Scheduled its workforce to provide faster, more accurate

answers to questions.

The US Internal Revenue Service needed to improve customer

service and...


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analyzed suspects’ cell phone usage to focus investigations.

The US Drug Enforcement Agency needed to be more effective in their drug “busts” and


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Reduced direct mail costs by 30% while garnering 95% of the

campaign’s revenue.

HSBC need to cross-sell more effectively by identifying profiles that would be interested in higheryielding investments and...


2012.11.01- SLIDE 80IS 257 – Fall 2012

Analytic technology can be effective

• Combining multiple models and link analysis can reduce false positives

• Today there are millions of false positives with manual analysis

• Data Mining is just one additional tool to help analysts

• Analytic Technology has the potential to reduce the current high rate of false positives


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Data Mining with Privacy

• Data Mining looks for patterns, not people!• Technical solutions can limit privacy

invasion– Replacing sensitive personal data with anon.

ID– Give randomized outputs– Multi-party computation – distributed data– …

• Bayardo & Srikant, Technological Solutions for Protecting Privacy, IEEE Computer, Sep 2003


2012.11.01- SLIDE 82IS 257 – Fall 2012

The Hype Curve for Data Mining and Knowledge Discovery

Over-inflated expectations

Disappointment

Growing acceptanceand mainstreaming

rising expectations


2012.11.01- slide 1is 257 – fall 2012 data warehouses, decision support and data mining university...

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